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arXiv · 2404.01257

New logarithmic step size for stochastic gradient descent

M. Soheil Shamaee·S. Fathi Hafshejani·Z. Saeidian
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Abstract

In this paper, we propose a novel warm restart technique using a new logarithmic step size for the stochastic gradient descent (SGD) approach. For smooth and non-convex functions, we establish an $O(\frac{1}{\sqrt{T}})$ convergence rate for the SGD. We conduct a comprehensive implementation to demonstrate the efficiency of the newly proposed step size on the ~FashionMinst,~ CIFAR10, and CIFAR100 datasets. Moreover, we compare our results with nine other existing approaches and demonstrate that the new logarithmic step size improves test accuracy by $0.9\%$ for the CIFAR100 dataset when we utilize a convolutional neural network (CNN) model.

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M. Soheil Shamaee, S. Fathi Hafshejani, Z. Saeidian. 2024-04-01. New logarithmic step size for stochastic gradient descent. https://doi.org/10.1007/s11704-023-3245-z

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